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Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models

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  • Demiralay, Sercan
  • Ulusoy, Veysel

Abstract

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and student-t innovations’ distributions. For these analyses, we consider both long and short trading positions. Overall, our results reveal that long memory volatility models under student-t distribution perform well in forecasting a one-day-ahead VaR for both long and short positions. In addition, we find that FIAPARCH model with student-t distribution, which jointly captures long memory and asymmetry, as well as fat-tails, outperforms other models in VaR forecasting. Our results have potential implications for portfolio managers, producers, and policy makers.

Suggested Citation

  • Demiralay, Sercan & Ulusoy, Veysel, 2014. "Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models," MPRA Paper 53229, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:53229
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    References listed on IDEAS

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    Cited by:

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    2. Abdelaziz Eissa, Mohamed & Al Refai, Hisham, 2024. "Context-dependent responses to geopolitical risk in Middle Eastern and African stock markets: An asymmetric volatility spillover study," International Review of Economics & Finance, Elsevier, vol. 94(C).
    3. Mesut BALLIBEY & Serpil T RKYILMAZ, 2014. "Value-at-Risk Analysis in the Presence of Asymmetry and Long Memory: The Case of Turkish Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 836-848.
    4. Sebastian Letmathe & Yuanhua Feng & André Uhde, 2021. "Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall," Working Papers CIE 141, Paderborn University, CIE Center for International Economics.

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    More about this item

    Keywords

    Long memory; value-at-risk; volatility modeling; precious metals prices;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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